Understanding the Racial and Income Gap in COVID‑19: Public Transportation and Home Crowding
This is the second post in a series that aims to understand the gap in COVID-19 intensity by race and income. In our post yesterday, we looked at how comorbidities, uninsurance rates, and health resources may help to explain the race and income gap observed in COVID-19 intensity. We found that a quarter of the income gap and more than a third of the racial gap in case rates are explained by health status and system factors. In this post, we look at two factors related to indoor density—namely the use of public transportation and increased home crowding. Here, we will aim to understand whether these two factors affect overall COVID-19 intensity, whether the income and racial gaps of COVID can be further explained when we additionally include these factors, and whether and to what extent these factors independently account for income and racial gaps in COVID-19 intensity (without controlling for the factors considered in the other posts in this series).
Understanding the Racial and Income Gap in Covid‑19: Health Insurance, Comorbidities, and Medical Facilities
Our previous work documents that low-income and majority-minority areas were considerably more affected by COVID-19, as captured by markedly higher case and death rates. In a four-part series starting with this post, we seek to understand the reasons behind these income and racial disparities. Do disparities in health status translate into disparities in COVID-19 intensity? Does the health system play a role through health insurance and hospital capacity? Can disparities in COVID-19 intensity be explained by high-density, crowded environments? Does social distancing, pollution, or the age composition of the county matter? Does the prevalence of essential service jobs make a difference? This post will focus on the first two questions. The next three posts in this series will focus on the remaining questions. The posts will follow a similar structure. In each post, we will aim to understand whether the factors considered in that post affect overall COVID-19 intensity, whether the racial and income gaps can be further explained when we additionally include the factors in consideration in that post, and whether and to what extent the factors under consideration in that post independently affect racial and income gaps in COVID-19 intensity (without controlling for the factors considered in the other posts in this series).
The Regional Economy during the Pandemic
The New York-Northern New Jersey region experienced an unprecedented downturn earlier this year, one more severe than that of the nation, and the region is still struggling to make up the ground that was lost. That is the key takeaway at an economic press briefing held today by the New York Fed examining economic conditions during the pandemic in the Federal Reserve’s Second District. Despite the substantial recovery so far, business activity, consumer spending, and employment are all still well below pre-pandemic levels in much of the region, and fiscal pressures are mounting for state and local governments. Importantly, job losses among lower-income workers and people of color have been particularly consequential. The pace of recovery was already slowing in the region before the most recent surge in coronavirus cases, and we are now seeing signs of renewed weakening as we enter the winter.
How Bank Reserves Are Distributed Matters. How You Measure Their Distribution Matters Too.
Changes in the distribution of banks’ reserve balances are important since they may impact conditions in the federal funds market and alter trading dynamics in money markets more generally. In this post, we propose using the Lorenz curve and Gini coefficient as a new approach to measuring reserve concentration. Since 2013, concentration, as captured by the Lorenz curve and the Gini coefficient, has co-moved with aggregate reserves, decreasing as aggregate reserves declined (such as in 2015-18) and increasing as aggregate reserves increased (such as at the onset of the COVID-19 pandemic).
The Disproportionate Effects of COVID‑19 on Households with Children
A growing body of evidence (here, here, and here) points to large negative economic and health impacts of the COVID-19 pandemic on low-income, Black, and Hispanic Americans. Beyond the consequences of school cancellations and lost social interactions, there exists considerable concern about the long-lasting effects of economic hardship on children. In this post, we assess the extent of the underlying economic and financial strain faced by households with children living at home, using newly collected data from the monthly Survey of Consumer Expectations (SCE).
Medicare and Financial Health across the United States
Consumer financial strain varies enormously across the United States. One pernicious source of financial strain is debt in collections—debt that is more than 120 days past due and that has been sold to a collections agency. In Massachusetts, the average person has less than $100 in collections debt, while in Texas, the average person has more than $300. In this post, we discuss our recent staff report that exploits the fact that virtually all Americans are universally covered by Medicare at 65 to show that health insurance not only improves financial health on average, but also is a major explanation for the heterogeneity in financial strain across the country. We find that Medicare affects different parts of the United States differently and plays a particularly important role in improving financial health in the least advantaged areas.